Technology innovations have extended life expectancy, improved health outcomes, and are reshaping the business of healthcare. Specifically, physicians, hospitals, and practices in general are moving toward an outcome-based model. In this model, improved health outcomes are prioritized over individual services and procedures.
This creates a new pressure on healthcare providers to analyze outcome metrics—looking for improvements and targeting areas for improvement. This model emphasizes preventative care and evaluating services based on their impact, rather than just their cost. This model favors the patient/customer and places a burden on providers to do more than simply charge for services rendered.
As the front line of patient care moves toward an outcome-based model, pressure is being felt throughout the greater healthcare economy. This includes medical equipment, devices, machines, and other assets used to deliver care. In the industrial medical equipment market, the healthcare provider becomes the customer. It is up to equipment manufacturers to deliver greater value—and enable their provider/customers to reach their own outcome-based goals.
But how exactly has this outcome-based market affected manufacturers and their go-to-market strategy? How can they innovate while controlling costs? Is it possible to turn these challenges into an opportunity for business growth? The answer lies in a predictive analytics healthcare technology strategy.
The practical application of predictive analytics is not new; the collection and analysis of big data to understand trends and predict behavior has been at the core of digital innovation for more than a decade. Some industries and verticals have been faster to adopt it than others. Certain areas of the healthcare industry, such as expensive diagnostic equipment have been pursuing innovation through connectivity and data analysis for some time.
Predictive analytics is intended to measure and understand data so that over time, trends, patterns, and logic can be identified in order to prevent negative outcomes. In manufacturing, this can be used internally to optimize operational performance in a factory. It can also be used to improve products and services, by ensuring products don’t fail in the field, or to anticipate replenishment of consumables.
While connectivity and data analysis are not unique to healthcare, the industry does have some unique applications, based on the larger outcome economy.
Connected medical, healthcare, and life sciences devices share some general attributes. They are typically technologically advanced and expensive. They are also relied upon by healthcare professionals for the critical tasks of collecting patient health metrics, administering therapies, and treating illnesses. The quality and purpose of these machines means that a predictive analytics healthcare strategy must have a unique, two-fold objective: using analytics to ensure uptime and performance, and to collect richer, more valuable and actionable patient health metrics.
Medical equipment comes at a premium, and the size of investments means that hospitals have a high expectation for value. When diagnostic and therapy equipment can cost in the millions of dollars, unplanned downtime translates to lost revenue. This is particularly true when the equipment is used to deliver critical healthcare needs.
To remain competitive and deliver value, the manufacturers of these devices are relying on remote condition monitoring to ensure that every aspect of the equipment is operating in a normal way. On top of this monitoring, analytics can be used to predict how likely, and how soon a piece of hardware may fail if unmaintained. In this way the provider can be assured that their investments will remain maintained, and when predictive maintenance is needed, it can be administered during off-peak hours, minimizing impact on patient care.
Predictive analytics and maintenance don’t just help the patient and caregivers; the manufacturers of this equipment can reassess the value of their offerings. The leaders in this market are moving towards usage-based, product-as-a-service model. Proactive maintenance is less expensive to administer and elevates the manufacturer’s role as a true solution provider, rather than just a supplier of commoditized products.
The outcome-based healthcare economy is reshaping care in notable ways, including remote monitoring of patients. Not unlike the monitoring of medical equipment, patients are outfitted with devices that track their activity, rest, and vital signs. In this way, “smart, connected patients” can be tracked and monitored.
Predictive analytics in healthcare can help identify at-risk patients in their homes to prevent hospital readmissions. By having access to data to identify patients that exhibit characteristics indicating a high likelihood of readmission, healthcare professionals would be able to create personalized healthcare protocols to decrease the likelihood of that patient’s return.
In other instances, say, a patient recovering from an extensive surgical heart procedure, if vital signs become abnormal, their healthcare providers can be notified and respond immediately. For the patient, this may be the difference between a drive to the doctor’s office, or an ambulance ride to the ER.
Predictive analytics are advancing so much so that providers are now collecting real-time data and looking for patterns or signals of problems before they occur. Remote monitoring applies to preventative care as well, helping patients with chronic conditions such as Type 2 diabetes, coronary artery disease, and COPD. Chronic diseases significantly affect patients’ lives and are the leading cause of death and disability in the U.S. Chronic conditions are also a costly issue for healthcare organizations and account for a disproportionate amount of healthcare expenditures. Fortunately, regardless of the specific patient diagnosis, predictive analytics healthcare capabilities are allowing healthcare providers not to merely respond to medical problems, but to prevent them before they occur.
ICUs are another area that would greatly benefit from predictive analytics. This has never been truer than during the current COVID-19 pandemic, when ICUs have had to deal with resource limitations, controlling infections, and protecting healthcare workers all at once. Predictive analytics makes it possible to estimate the risk of patient health deterioration or a poor outcome, something that healthcare workers are faced with when having to allocate limited healthcare resources. These predictive models are also beneficial in the planning phase—in order to be able to allocate resources from personnel to hospital beds to ICU capacity. Although many American healthcare systems do not have all the technology in place to use predictive analytics during this crisis, there is no doubt about the critical need for it to be implemented ahead of the next disruption.
The process toward an outcome-based healthcare system puts pressure on providers to rethink their business. They are rethinking not just the services they provide, but the equipment and assets that enable those services. For manufacturers of complex medical equipment and devices, this means delivering more reliable solutions.
To learn more about predictive healthcare analytics, read the Axendia IoMT Impact Survey.